Underspecified queries are common in vertical search engines, leading to large
result sets
that are difficult for users to navigate. In this paper, we show that we can
automatically guide users to their target results by engaging them in a dialog
consisting of well-formed binary questions mined from unstructured data. We
propose a system that extracts candidate attribute-value question terms from
unstructured descriptions of records in a database.
These terms are then filtered using a Maximum Entropy classifier to identify
those that
are suitable for question formation given a user query. We then select question
terms via
a novel ranking function that aims to minimize the number of question turns
necessary
for a user to find her target result. We evaluate the quality of system-generated
questions
for grammaticality and refinement effectiveness. Our final system shows best
results in
effectiveness, percentage of well-formed questions, and percentage of answerable
questions
over three baseline systems.